Image Super Resolution On Urban100 3X

Métriques

PSNR

SSIM

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	PSNR	SSIM	Paper Title	Repository
SwinFIR	30.43	0.8913	SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
HMA†	31.00	0.8984	HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution
HAT	30.70	0.8949	Activating More Pixels in Image Super-Resolution Transformer
Hi-IR-L	31.07	0.902	Hierarchical Information Flow for Generalized Efficient Image Restoration	-
IMDN	28.17	-	Lightweight Image Super-Resolution with Information Multi-distillation Network
DnCNN-3	27.15	-	Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
CPAT+	30.63	0.8934	Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution	-
ML-CrAIST-Li	28.73	0.8651	ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer
HAN+	29.21	0.8710	Single Image Super-Resolution via a Holistic Attention Network
SRFBN	28.73	-	Feedback Network for Image Super-Resolution
FACD	28.818	-	Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution	-
IPT	29.49	-	Pre-Trained Image Processing Transformer
CSNLN	29.13	0.8712	Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
LCSCNet	27.24	-	LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution
LTE	29.41	-	Local Texture Estimator for Implicit Representation Function
HAT-L	30.92	0.8981	Activating More Pixels in Image Super-Resolution Transformer
ML-CrAIST	28.89	0.8676	ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer
SwinOIR	28.87	0.8674	Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy
HAT_FIR	30.77	-	SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
CPAT	30.52	0.8923	Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution	-

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